Field
Certain aspects of the present disclosure generally relate to machine learning and, more particularly, to improving systems and methods of predicting labels for a video stream.
Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (e.g., neuron models), is a computational device or represents a method to be performed by a computational device.
Convolutional neural networks are a type of feed-forward artificial neural network. Convolutional neural networks may include collections of neurons that each has a receptive field and that collectively tile an input space. Convolutional neural networks (CNNs) have numerous applications. In particular, CNNs have broadly been used in the area of pattern recognition and classification.
Recurrent neural networks (RNNs) are a class of neural network, which includes a cyclical connection between nodes or units of the network. The cyclical connection creates an internal state that may serve as a memory that enables RNNs to model dynamical systems. That is, these cyclical connections offer RNNs the ability to encode memory and as such, these networks, if successfully trained, are suitable for sequence learning applications.
Long short-term memory (LSTM) is a type of RNN in a microcircuit composed of multiple units to store values in memory using gating functions and multipliers. LSTMs are able to hold a value in memory for an arbitrary length of time. As such, LSTMs may be useful in learning, classification systems (e.g., handwriting and speech recognition systems), and other applications.